185 research outputs found
Hirschman optimal transform least mean square adaptive filters.
Abstract not available
On adaptive filter structure and performance
SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Acoustic echo cancellation for full-duplex voice transmission on fading channels
This paper discusses the implementation of an adaptive acoustic echo canceler for a hands-free cellular phone operating on a fading channel. The adaptive lattice structure, which is particularly known for faster convergence relative to the conventional tapped-delay-line (TDL) structure, is used in the initialization stage. After convergence, the lattice coefficients are converted into the coefficients for the TDL structure which can accommodate a larger number of taps in real-time operation due to its computational simplicity. The conversion method of the TDL coefficients from the lattice coefficients is derived and the DSP56001 assembly code for the lattice and TDL structure is included, as well as simulation results and the schematic diagram for the hardware implementation
Frequency domain FIR and IIR adaptive filters
A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented. This is used to introduce the concept of proportional convergence. An approach is used to analyze the convergence characteristics of block frequency-domain adaptive filters. This leads to a development showing how the frequency-domain FIR adaptive filter is easily modified to provide proportional convergence. These ideas are extended to a block frequency-domain IIR adaptive filter and the idea of proportional convergence is applied. Experimental results illustrating proportional convergence in both FIR and IIR frequency-domain block adaptive filters is presented
Full-Duplex OFDM Radar With LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements
This paper studies the processing principles, implementation challenges, and
performance of OFDM-based radars, with particular focus on the
fourth-generation Long-Term Evolution (LTE) and fifth-generation (5G) New Radio
(NR) mobile networks' base stations and their utilization for radar/sensing
purposes. First, we address the problem stemming from the unused subcarriers
within the LTE and NR transmit signal passbands, and their impact on
frequency-domain radar processing. Particularly, we formulate and adopt a
computationally efficient interpolation approach to mitigate the effects of
such empty subcarriers in the radar processing. We evaluate the target
detection and the corresponding range and velocity estimation performance
through computer simulations, and show that high-quality target detection as
well as high-precision range and velocity estimation can be achieved.
Especially 5G NR waveforms, through their impressive channel bandwidths and
configurable subcarrier spacing, are shown to provide very good radar/sensing
performance. Then, a fundamental implementation challenge of
transmitter-receiver (TX-RX) isolation in OFDM radars is addressed, with
specific emphasis on shared-antenna cases, where the TX-RX isolation challenges
are the largest. It is confirmed that from the OFDM radar processing
perspective, limited TX-RX isolation is primarily a concern in detection of
static targets while moving targets are inherently more robust to transmitter
self-interference. Properly tailored analog/RF and digital self-interference
cancellation solutions for OFDM radars are also described and implemented, and
shown through RF measurements to be key technical ingredients for practical
deployments, particularly from static and slowly moving targets' point of view.Comment: Paper accepted by IEEE Transactions on Microwave Theory and
Technique
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
With the aim of developing a fast yet accurate algorithm for compressive
sensing (CS) reconstruction of natural images, we combine in this paper the
merits of two existing categories of CS methods: the structure insights of
traditional optimization-based methods and the speed of recent network-based
ones. Specifically, we propose a novel structured deep network, dubbed
ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm
(ISTA) for optimizing a general norm CS reconstruction model. To cast
ISTA into deep network form, we develop an effective strategy to solve the
proximal mapping associated with the sparsity-inducing regularizer using
nonlinear transforms. All the parameters in ISTA-Net (\eg nonlinear transforms,
shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than
being hand-crafted. Moreover, considering that the residuals of natural images
are more compressible, an enhanced version of ISTA-Net in the residual domain,
dubbed {ISTA-Net}, is derived to further improve CS reconstruction.
Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform
existing state-of-the-art optimization-based and network-based CS methods by
large margins, while maintaining fast computational speed. Our source codes are
available: \textsl{http://jianzhang.tech/projects/ISTA-Net}.Comment: 10 pages, 6 figures, 4 Tables. To appear in CVPR 201
Robust orthogonal parameterization of evolution strategy for adaptive laser pulse shaping
Many spectroscopic applications of femtosecond laser pulses require properly-shaped spectral phase profiles. The optimal phase profile can be programmed on the pulse by adaptive pulse shaping. A promising optimization algorithm for such adaptive experiments is evolution strategy (ES). Here, we report a four fold increase in the rate of convergence and ten percent increase in the final yield of the optimization, compared to the direct parameterization approach, by using a new version of ES in combination with Legendre polynomials and frequency-resolved detection. Such a fast learning rate is of paramount importance in spectroscopy for reducing the artifacts of laser drift, optical degradation, and precipitation
- …